188: Authentication of Grass-Finished Beef Using Targeted Gene Profiling

Introduction

Research demonstrates that cattle grazed on phytochemically diverse pastures results in beef that aligns with human health recommendations—boasting a lower omega-6:omega-3 (n-6:n-3) ratio (approximately 2:1) compared to grain-finished beef. However, a recent nutritional survey revealed significant variations in n-6:n-3 ratios among grass-finished beef from different U.S. producers. Additionally, the U.S. market imports 70-80% of its grass-finished beef, raising concerns about the accuracy of producers’ claimed grazing practices. This highlights the need for reliable methods to authenticate grass-finished beef and distinguish it from other feeding systems.

Methods

This two-year study used 54 Red Angus steers randomly allocated to one of three finishing diets: (1) grazing a biodiverse pasture (GRASS), (2) grain in feedlot (GRAIN), or (3) grain in feedlot + grape seed extract (GRAPE). Real-time PCR was performed on cDNA extracts from frozen ribeye samples (n=54) targeting four genes (EIF4EBP1, ALAD, NPNT-1, PPARG) and two reference genes (YWHAZ, RPLP0). Relative gene expression was calculated using the ∆∆Ct method. Statistical analysis included a linear regression model (diet, year, pen as fixed effects; animal as experimental unit) and post-hoc Tukey’s tests with Dunn-Šidák correction to compare gene expression across dietary groups.

Results

For PPARG, beef from the GRASS group showed significantly higher relative expression than the GRAPE group (p = 0.038). For EIF4EBP1, ALAD, and NPNT-1, GRASS beef consistently exhibited higher relative expression compared to GRAIN and GRAPE (p < 0.05 for all), with no significant differences between GRAIN and GRAPE (p > 0.05).

Significance

This study demonstrates the potential of PCR-based methods to authenticate grass-finished beef, ensuring the accuracy of grazing claims while enhancing producer credibility and consumer confidence. Future research should develop rapid, reliable authentication methods applicable for industry use.

Authors: Chloe Castanon, Dr. Lucas Krusinski, Dr. Rosalee S. Hellberg